增加log，修改corner

brep2sdf/data/data.py

@@ -3,10 +3,8 @@ import torch
 from torch.utils.data import Dataset
 import numpy as np
 import pickle
-from brep2sdf.utils.logger import setup_logger
+from brep2sdf.utils.logger import logger
-from .utils import process_brep_data
+from brep2sdf.data.utils import process_brep_data
-# 设置日志记录器
-logger = setup_logger('dataset')
 
 
 
@@ -22,10 +20,16 @@ class BRepSDFDataset(Dataset):
             sdf_dir: npz文件目录
             split: 数据集分割('train', 'val', 'test')
         """
+        super().__init__()
         self.brep_dir = os.path.join(brep_dir, split)
         self.sdf_dir = os.path.join(sdf_dir, split)
         self.split = split
         
+        # 设置固定参数
+        self.max_face = 70
+        self.max_edge = 70
+        self.bbox_scaled = 1.0
+        
         # 检查目录是否存在
         if not os.path.exists(self.brep_dir):
             raise ValueError(f"B-rep directory not found: {self.brep_dir}")
@@ -59,112 +63,187 @@ class BRepSDFDataset(Dataset):
 
     def __getitem__(self, idx):
         """获取单个数据样本"""
-        brep_path = self.brep_data_list[idx]
-        sdf_path = self.sdf_data_list[idx]
-        
         try:
-            # 获取文件名(不含扩展名)作为sample name
+            brep_path = self.brep_data_list[idx]
+            sdf_path = self.sdf_data_list[idx]
             name = os.path.splitext(os.path.basename(brep_path))[0]
             
             # 加载B-rep和SDF数据
-            brep_data = self._load_brep_file(brep_path)
+            with open(brep_path, 'rb') as f:
+                brep_raw = pickle.load(f)
             sdf_data = self._load_sdf_file(sdf_path)
-            
+
-            # 修改返回格式，将sdf_data作为一个键值对添加
+            try:
-            return {
+                # 处理B-rep数据
-                'name': name,
+                brep_features = process_brep_data(
-                **brep_data,  # 解包B-rep特征
+                    data=brep_raw,
-                'sdf': sdf_data  # 添加SDF数据作为一个键
+                    max_face=self.max_face,
-            }
+                    max_edge=self.max_edge,
-            
+                    bbox_scaled=self.bbox_scaled
+                )
+                
+                # 检查返回值的类型和数量
+                if not isinstance(brep_features, tuple):
+                    logger.error(f"process_brep_data returned {type(brep_features)}, expected tuple")
+                    raise ValueError("Invalid return type from process_brep_data")
+                    
+                if len(brep_features) != 6:
+                    logger.error(f"Expected 6 features, got {len(brep_features)}")
+                    logger.error("Features returned:")
+                    for i, feat in enumerate(brep_features):
+                        if isinstance(feat, torch.Tensor):
+                            logger.error(f"  {i}: Tensor of shape {feat.shape}")
+                        else:
+                            logger.error(f"  {i}: {type(feat)}")
+                    raise ValueError(f"Incorrect number of features: {len(brep_features)}")
+                
+                # 解包处理后的特征
+                edge_ncs, edge_pos, edge_mask, surf_ncs, surf_pos, vertex_pos = brep_features
+                
+                # 构建返回字典
+                return {
+                    'name': name,
+                    'edge_ncs': edge_ncs,      # [max_face, max_edge, 10, 3]
+                    'edge_pos': edge_pos,      # [max_face, max_edge, 6]
+                    'edge_mask': edge_mask,    # [max_face, max_edge]
+                    'surf_ncs': surf_ncs,      # [max_face, 100, 3]
+                    'surf_pos': surf_pos,      # [max_face, 6]
+                    'vertex_pos': vertex_pos,  # [max_face, max_edge, 6]
+                    'sdf': sdf_data           # [N, 4]
+                }
+                
+            except Exception as e:
+                logger.error(f"\nError processing B-rep data for file: {brep_path}")
+                logger.error(f"Error type: {type(e).__name__}")
+                logger.error(f"Error message: {str(e)}")
+                
+                # 打印原始数据的结构
+                logger.error("\nRaw data structure:")
+                for key, value in brep_raw.items():
+                    if isinstance(value, list):
+                        logger.error(f"  {key}: list of length {len(value)}")
+                        if value:
+                            logger.error(f"    First element type: {type(value[0])}")
+                            if hasattr(value[0], 'shape'):
+                                logger.error(f"    First element shape: {value[0].shape}")
+                    elif hasattr(value, 'shape'):
+                        logger.error(f"  {key}: shape {value.shape}")
+                    else:
+                        logger.error(f"  {key}: {type(value)}")
+                raise
+                
         except Exception as e:
             logger.error(f"Error loading sample from {brep_path}: {str(e)}")
             logger.error("Data structure:")
-            if 'brep_data' in locals():
-                for key, value in brep_data.items():
-                    if isinstance(value, np.ndarray):
-                        logger.error(f"  {key}: type={type(value)}, dtype={value.dtype}, shape={value.shape}")
             raise
 
 
     def _load_brep_file(self, brep_path):
         """加载B-rep特征文件"""
         try:
+            # 1. 加载原始数据
             with open(brep_path, 'rb') as f:
-                brep_data = pickle.load(f)
+                raw_data = pickle.load(f)
             
             brep_data = {}
             
-            # 1. 处理几何数据（不等长序列）
+            # 2. 处理几何数据（不等长序列）
-            for key in ['surf_wcs', 'surf_ncs', 'edge_wcs', 'edge_ncs']:
+            geom_keys = ['surf_wcs', 'surf_ncs', 'edge_wcs', 'edge_ncs']
-                if key in brep_data:
+            for key in geom_keys:
+                if key in raw_data:
                     try:
-                        brep_data[key] = [
+                        # 确保数据是列表
-                            torch.from_numpy(np.array(x, dtype=np.float32)) 
+                        if not isinstance(raw_data[key], list):
-                            for x in brep_data[key]
+                            raise ValueError(f"{key} is not a list")
-                        ]
+                            
+                        # 转换每个元素为张量
+                        tensors = []
+                        for i, x in enumerate(raw_data[key]):
+                            try:
+                                # 先转换为numpy数组
+                                arr = np.array(x, dtype=np.float32)
+                                # 再转换为张量
+                                tensor = torch.from_numpy(arr)
+                                tensors.append(tensor)
+                            except Exception as e:
+                                logger.error(f"Error converting {key}[{i}]:")
+                                logger.error(f"  Data type: {type(x)}")
+                                if isinstance(x, np.ndarray):
+                                    logger.error(f"  Shape: {x.shape}")
+                                    logger.error(f"  dtype: {x.dtype}")
+                                raise ValueError(f"Failed to convert {key}[{i}]: {str(e)}")
+                                
+                        brep_data[key] = tensors
+                        
                     except Exception as e:
-                        logger.error(f"Error converting {key}:")
+                        logger.error(f"Error processing {key}:")
-                        logger.error(f"  Type: {type(brep_data[key])}")
+                        logger.error(f"  Raw data type: {type(raw_data[key])}")
-                        if isinstance(brep_data[key], list):
+                        raise ValueError(f"Failed to process {key}: {str(e)}")
-                            logger.error(f"  List length: {len(brep_data[key])}")
-                            if len(brep_data[key]) > 0:
-                                logger.error(f"  First element type: {type(brep_data[key][0])}")
-                                logger.error(f"  First element shape: {brep_data[key][0].shape}")
-                                logger.error(f"  First element dtype: {brep_data[key][0].dtype}")
-                        raise ValueError(f"Failed to convert {key}: {str(e)}")
             
-            # 2. 处理固定形状的数据
+            # 3. 处理固定形状的数据
-            for key in ['corner_wcs', 'corner_unique', 'surf_bbox_wcs', 'edge_bbox_wcs']:
+            fixed_keys = ['corner_wcs', 'corner_unique', 'surf_bbox_wcs', 'edge_bbox_wcs']
-                if key in brep_data:
+            for key in fixed_keys:
+                if key in raw_data:
                     try:
-                        data = np.array(brep_data[key], dtype=np.float32)
+                        # 直接从原始数据转换
-                        brep_data[key] = torch.from_numpy(data)
+                        arr = np.array(raw_data[key], dtype=np.float32)
+                        brep_data[key] = torch.from_numpy(arr)
                     except Exception as e:
-                        logger.error(f"Error converting {key}:")
+                        logger.error(f"Error converting fixed shape data {key}:")
-                        logger.error(f"  Type: {type(brep_data[key])}")
+                        logger.error(f"  Raw data type: {type(raw_data[key])}")
-                        if isinstance(brep_data[key], np.ndarray):
+                        if isinstance(raw_data[key], np.ndarray):
-                            logger.error(f"  Shape: {brep_data[key].shape}")
+                            logger.error(f"  Shape: {raw_data[key].shape}")
-                            logger.error(f"  dtype: {brep_data[key].dtype}")
+                            logger.error(f"  dtype: {raw_data[key].dtype}")
                         raise ValueError(f"Failed to convert {key}: {str(e)}")
             
-            # 3. 处理邻接矩阵
+            # 4. 处理邻接矩阵
-            for key in ['edgeFace_adj', 'edgeCorner_adj', 'faceEdge_adj']:
+            adj_keys = ['edgeFace_adj', 'edgeCorner_adj', 'faceEdge_adj']
-                if key in brep_data:
+            for key in adj_keys:
+                if key in raw_data:
                     try:
-                        data = np.array(brep_data[key], dtype=np.int32)
+                        # 转换为整型数组
-                        brep_data[key] = torch.from_numpy(data)
+                        arr = np.array(raw_data[key], dtype=np.int32)
+                        brep_data[key] = torch.from_numpy(arr)
                     except Exception as e:
-                        logger.error(f"Error converting {key}:")
+                        logger.error(f"Error converting adjacency matrix {key}:")
-                        logger.error(f"  Type: {type(brep_data[key])}")
+                        logger.error(f"  Raw data type: {type(raw_data[key])}")
-                        if isinstance(brep_data[key], np.ndarray):
+                        if isinstance(raw_data[key], np.ndarray):
-                            logger.error(f"  Shape: {brep_data[key].shape}")
+                            logger.error(f"  Shape: {raw_data[key].shape}")
-                            logger.error(f"  dtype: {brep_data[key].dtype}")
+                            logger.error(f"  dtype: {raw_data[key].dtype}")
                         raise ValueError(f"Failed to convert {key}: {str(e)}")
-            feature_embedder = process_brep_data(brep_data, 70,70,1,)
-
-            return feature_embedder
             
+            # 5. 验证必要的键是否存在
+            required_keys = {'surf_wcs', 'edge_wcs', 'corner_wcs'}
+            missing_keys = required_keys - set(brep_data.keys())
+            if missing_keys:
+                raise ValueError(f"Missing required keys: {missing_keys}")
+            
+            # 6. 使用process_brep_data处理数据
+            try:
+                features = process_brep_data(
+                    data=brep_data,
+                    max_face=self.max_face,
+                    max_edge=self.max_edge,
+                    bbox_scaled=self.bbox_scaled
+                )
+                return features
+                
+            except Exception as e:
+                logger.error("Error in process_brep_data:")
+                logger.error(f"  Error message: {str(e)}")
+                # 打印数据形状信息
+                logger.error("\nInput data shapes:")
+                for key, value in brep_data.items():
+                    if isinstance(value, list):
+                        shapes = [t.shape for t in value]
+                        logger.error(f"  {key}: list of tensors with shapes {shapes}")
+                    elif isinstance(value, torch.Tensor):
+                        logger.error(f"  {key}: tensor of shape {value.shape}")
+                raise
+                
         except Exception as e:
             logger.error(f"\nError loading B-rep file: {brep_path}")
             logger.error(f"Error message: {str(e)}")
-            
-            # 打印完整的数据结构信息
-            if 'brep_data' in locals():
-                logger.error("\nComplete data structure:")
-                for key, value in brep_data.items():
-                    logger.error(f"\n{key}:")
-                    logger.error(f"  Type: {type(value)}")
-                    if isinstance(value, np.ndarray):
-                        logger.error(f"  Shape: {value.shape}")
-                        logger.error(f"  dtype: {value.dtype}")
-                    elif isinstance(value, list):
-                        logger.error(f"  List length: {len(value)}")
-                        if len(value) > 0:
-                            logger.error(f"  First element type: {type(value[0])}")
-                            if isinstance(value[0], np.ndarray):
-                                logger.error(f"  First element shape: {value[0].shape}")
-                                logger.error(f"  First element dtype: {value[0].dtype}")
             raise
 
     def _load_sdf_file(self, sdf_path):
@@ -190,6 +269,52 @@ class BRepSDFDataset(Dataset):
             logger.error(f"Error type: {type(e).__name__}")
             logger.error(f"Error message: {str(e)}")
             raise
+    @staticmethod
+    def collate_fn(batch):
+        """自定义批处理函数"""
+        # 收集所有样本的名称
+        names = [item['name'] for item in batch]
+        
+        # 处理固定大小的张量数据
+        tensor_keys = ['edge_ncs', 'edge_pos', 'edge_mask', 
+                      'surf_ncs', 'surf_pos', 'vertex_pos']
+        tensors = {
+            key: torch.stack([item[key] for item in batch])
+            for key in tensor_keys
+        }
+        
+        # 处理变长的SDF数据
+        sdf_data = [item['sdf'] for item in batch]
+        max_sdf_len = max(sdf.size(0) for sdf in sdf_data)
+        
+        # 填充SDF数据
+        padded_sdfs = []
+        sdf_masks = []
+        for sdf in sdf_data:
+            pad_len = max_sdf_len - sdf.size(0)
+            if pad_len > 0:
+                padding = torch.zeros(pad_len, sdf.size(1), 
+                                   dtype=sdf.dtype, device=sdf.device)
+                padded_sdf = torch.cat([sdf, padding], dim=0)
+                mask = torch.cat([
+                    torch.ones(sdf.size(0), dtype=torch.bool),
+                    torch.zeros(pad_len, dtype=torch.bool)
+                ])
+            else:
+                padded_sdf = sdf
+                mask = torch.ones(sdf.size(0), dtype=torch.bool)
+            padded_sdfs.append(padded_sdf)
+            sdf_masks.append(mask)
+        
+        # 合并所有数据
+        batch_data = {
+            'name': names,
+            'sdf': torch.stack(padded_sdfs),
+            'sdf_mask': torch.stack(sdf_masks),
+            **tensors
+        }
+        
+        return batch_data
 
 def test_dataset():
     """测试数据集功能"""

brep2sdf/data/utils.py

@@ -8,6 +8,8 @@ import argparse
 from chamferdist import ChamferDistance
 from mpl_toolkits.mplot3d.art3d import Poly3DCollection
 from typing import List, Optional, Tuple, Union
+from brep2sdf.utils.logger import logger
+
 
 from OCC.Core.gp import gp_Pnt, gp_Pnt
 from OCC.Core.TColgp import TColgp_Array2OfPnt
@@ -986,8 +988,16 @@ def process_brep_data(
             - data_class: (可选) 类别标签 [1]
     """
     # 解包数据
-    _, _, surf_ncs, edge_ncs, corner_wcs, _, _, faceEdge_adj, surf_pos, edge_pos, _, _ = data.values()
+    #_, _, surf_ncs, edge_ncs, corner_wcs, _, _, faceEdge_adj, surf_pos, edge_pos, _ = data.values()
-    
+    # 直接获取需要的数据
+    surf_ncs = data['surf_ncs']         # (num_faces,) -> 每个元素形状 (N, 3)
+    edge_ncs = data['edge_ncs']         # (num_edges, 100, 3)
+    corner_wcs = data['corner_wcs']     # (num_corners, 3)
+    faceEdge_adj = data['faceEdge_adj'] # (num_faces, num_edges)
+    edgeCorner_adj = data['edgeCorner_adj'] # (num_edges, 2) 每条边连接2个顶点
+    surf_pos = data['surf_bbox_wcs']    # (num_faces, 6)
+    edge_pos = data['edge_bbox_wcs']    # (num_edges, 6)
+
     # 数据增强
     random_num = np.random.rand()
     if random_num > 0.5 and aug:
@@ -1002,29 +1012,38 @@ def process_brep_data(
             surfpos_corners = rotate_axis(surfpos_corners, angle, axis, normalized=True)
             edgepos_corners = rotate_axis(edgepos_corners, angle, axis, normalized=True)
             corner_wcs = rotate_axis(corner_wcs, angle, axis, normalized=True)
-            surf_ncs = rotate_axis(surf_ncs, angle, axis, normalized=False)
+            
+            # 对每个面的点云进行旋转
+            for i in range(len(surf_ncs)):
+                surf_ncs[i] = rotate_axis(surf_ncs[i], angle, axis, normalized=False)
             edge_ncs = rotate_axis(edge_ncs, angle, axis, normalized=False)
         
+        
         # 重新计算边界框
         surf_pos = get_bbox(surfpos_corners)  # [num_faces, 2, 3]
         surf_pos = surf_pos.reshape(len(surf_pos), 6)  # [num_faces, 6]
         edge_pos = get_bbox(edgepos_corners)  # [num_edges, 2, 3]
         edge_pos = edge_pos.reshape(len(edge_pos), 6)  # [num_edges, 6]
-    
+
     # 缩放值范围
     surf_pos = surf_pos * bbox_scaled  # [num_faces, 6]
     edge_pos = edge_pos * bbox_scaled  # [num_edges, 6]
     corner_wcs = corner_wcs * bbox_scaled  # [num_edges, 2, 3]
     
     # 特征复制
-    edge_pos_duplicated: List[np.ndarray] = []  # 每个元素形状: [num_edges_per_face, 6]
+    edge_pos_duplicated: List[np.ndarray] = []  # 每个元素形状: [num_edges, 6]
-    vertex_pos_duplicated: List[np.ndarray] = []  # 每个元素形状: [num_edges_per_face, 6]
+    vertex_pos_duplicated: List[np.ndarray] = []  # 每个元素形状: [num_edges, 6]
-    edge_ncs_duplicated: List[np.ndarray] = []  # 每个元素形状: [num_edges_per_face, 10, 3]
+    edge_ncs_duplicated: List[np.ndarray] = []  # 每个元素形状: [num_edges, 10, 3]
     
     for adj in faceEdge_adj:  # [num_faces, num_edges]
-        edge_ncs_duplicated.append(edge_ncs[adj])  # [num_edges_per_face, 10, 3]
+        edge_ncs_duplicated.append(edge_ncs[adj])  # [num_edges, 10, 3]
-        edge_pos_duplicated.append(edge_pos[adj])  # [num_edges_per_face, 6]
+        edge_pos_duplicated.append(edge_pos[adj])  # [num_edges, 6]
-        corners = corner_wcs[adj]  # [num_edges_per_face, 2, 3]
+        #corners = corner_wcs[adj]  # [num_vertax, 3] FIXME
+        edge_indices = np.where(adj)[0]              # 获取当前面的边索引
+        corner_indices = edgeCorner_adj[edge_indices] # 获取这些边对应的顶点索引
+        corners = corner_wcs[corner_indices]          # 获取顶点坐标
+        logger.debug(corners)
+
         corners_sorted = []
         for corner in corners:  # [2, 3]
             sorted_indices = np.lexsort((corner[:, 2], corner[:, 1], corner[:, 0]))
@@ -1062,7 +1081,10 @@ def process_brep_data(
     random_indices = np.random.permutation(surf_pos.shape[0])
     surf_pos = surf_pos[random_indices]      # [num_faces, 6]
     edge_pos = edge_pos[random_indices]      # [num_faces, max_edge, 6]
-    surf_ncs = surf_ncs[random_indices]      # [num_faces, 100, 3]
+    # 修改这里：surf_ncs是对象数组，需要特殊处理
+    surf_ncs_new = np.array([surf_ncs[i] for i in random_indices])  # 重新排列对象数组
+    surf_ncs = surf_ncs_new
+    #surf_ncs = surf_ncs[random_indices]      # [num_faces, 100, 3]
     edge_ncs = edge_ncs[random_indices]      # [num_faces, max_edge, 10, 3]
     edge_mask = edge_mask[random_indices]    # [num_faces, max_edge]
     vertex_pos = vertex_pos[random_indices]  # [num_faces, max_edge, 6]

brep2sdf/scripts/process_brep.py

@@ -184,32 +184,27 @@ def get_bbox(shape, subshape):
 
 
 def parse_solid(step_path):
-    """Parse the surface, curve, face, edge, vertex in a CAD solid using OCC."""
     """
-    返回值：一个dict，包括
+    解析STEP文件中的CAD模型数据
-    几何关系
+    
-        # 面片数据
+    返回:
-        'surf_wcs': surfs_wcs,    # list of np.array(N, 3), 每个面片的点云坐标
+    dict: 包含以下键值对的字典:
-        'surf_ncs': surfs_ncs,    # list of np.array(N, 3), 每个面片的法向量
+        # 几何数据
+        'surf_wcs': np.ndarray(dtype=object)     # 形状为(N,)的数组，每个元素是形状为(M, 3)的float32数组，表示面的点云坐标
+        'edge_wcs': np.ndarray(dtype=object)     # 形状为(N,)的数组，每个元素是形状为(100, 3)的float32数组，表示边的采样点坐标
+        'surf_ncs': np.ndarray(dtype=object)     # 形状为(N,)的数组，每个元素是形状为(M, 3)的float32数组，表示归一化后的面点云
+        'edge_ncs': np.ndarray(dtype=object)     # 形状为(N,)的数组，每个元素是形状为(100, 3)的float32数组，表示归一化后的边采样点
+        'corner_wcs': np.ndarray(dtype=float32)  # 形状为(K, 3)的数组，表示所有顶点的坐标
+        'corner_unique': np.ndarray(dtype=float32) # 形状为(L, 3)的数组，表示去重后的顶点坐标
         
-        # 边数据 (num_samples=100)
+        # 拓扑关系
-        'edge_wcs': edges_wcs,    # list of np.array(100, 3), 每条边的采样点坐标
+        'edgeFace_adj': np.ndarray(dtype=int32)  # 形状为(num_edges, num_faces)的数组，表示边-面邻接关系
-        'edge_ncs': edges_ncs,    # list of np.array(100, 3), 每条边的法向量
+        'edgeCorner_adj': np.ndarray(dtype=int32) # 形状为(num_edges, 2)的数组，表示边-顶点邻接关系
+        'faceEdge_adj': np.ndarray(dtype=int32)  # 形状为(num_faces, num_edges)的数组，表示面-边邻接关系
         
-        # 顶点数据
+        # 包围盒数据
-        'corner_wcs': corner_wcs.astype(np.float32),      # np.array(N, 3), 所有顶点坐标
+        'surf_bbox_wcs': np.ndarray(dtype=float32) # 形状为(num_faces, 6)的数组，表示每个面的包围盒[xmin,ymin,zmin,xmax,ymax,zmax]
-        'corner_unique': np.unique(corner_wcs, axis=0).astype(np.float32),  # np.array(M, 3), 去重后的顶点坐标
+        'edge_bbox_wcs': np.ndarray(dtype=float32) # 形状为(num_edges, 6)的数组，表示每条边的包围盒[xmin,ymin,zmin,xmax,ymax,zmax]
-    拓扑关系
-        # 邻接矩阵，都是int32类型
-        'edgeFace_adj': edgeFace_adj,    # np.array(num_edges, num_faces), 边-面邻接关系
-        'edgeCorner_adj': edgeCorner_adj, # np.array(num_edges, 2), 边-顶点邻接关系
-        'faceEdge_adj': faceEdge_adj,     # np.array(num_faces, num_edges), 面-边邻接关系
-    包围盒数据
-        # 包围盒坐标，float32类型
-        'surf_bbox_wcs': surf_bbox_wcs,   # np.array(num_faces, 6), 每个面的包围盒 [xmin,ymin,zmin,xmax,ymax,zmax]
-        'edge_bbox_wcs': edge_bbox_wcs,   # np.array(num_edges, 6), 每条边的包围盒 [xmin,ymin,zmin,xmax,ymax,zmax]
-    
-
     """
     # Load STEP file
     reader = STEPControl_Reader()

brep2sdf/scripts/read_pkl.py

@@ -8,7 +8,6 @@ def inspect_data(pkl_file):
     """检查并显示pickle文件中的数据结构"""
     with open(pkl_file, 'rb') as f:
         data = pickle.load(f)
-    
     print("数据结构概览:")
     print("=" * 50)
     
@@ -36,5 +35,5 @@ def inspect_data(pkl_file):
             print(f"值: {value}")
 
 if __name__ == "__main__":
-    pkl_file = "./bathtub_0011.pkl"  # 替换为你的文件路径
+    pkl_file = "/home/wch/brep2sdf/test_data/pkl/train/bathtub_0004.pkl"  # 替换为你的文件路径
     inspect_data(pkl_file)

brep2sdf/utils/logger.py

@@ -1,46 +1,82 @@
 import os
+import sys
 import logging
+import traceback
 from datetime import datetime
 
-def setup_logger(name, log_dir='logs'):
+class BRepLogger:
-    """
+    _instance = None
-    设置日志记录器
     
-    参数:
+    def __new__(cls):
-        name: 日志记录器名称
+        if cls._instance is None:
-        log_dir: 日志文件存储目录
+            cls._instance = super().__new__(cls)
-        
+            cls._instance._initialize_logger()
-    返回:
+        return cls._instance
-        logger: 配置好的日志记录器
-    """
-    # 创建logs目录
-    os.makedirs(log_dir, exist_ok=True)
-    
-    # 创建日志记录器
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.INFO)
     
-    # 如果logger已经有处理器，则不添加
+    def _initialize_logger(self):
-    if logger.handlers:
+        """初始化日志记录器"""
-        return logger
+        # 创建logs目录
+        log_dir = 'logs'
+        os.makedirs(log_dir, exist_ok=True)
+        
+        # 创建logger
+        self.logger = logging.getLogger('BRepLogger')
+        self.logger.setLevel(logging.DEBUG)
+        
+        # 如果logger已经有处理器，则返回
+        if self.logger.handlers:
+            return
+            
+        # 创建格式化器
+        formatter = logging.Formatter(
+            '%(asctime)s - %(levelname)s - '
+            '%(filename)s:%(lineno)d in %(funcName)s - %(message)s'
+        )
+        
+        # 创建文件处理器
+        current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
+        log_file = os.path.join(log_dir, f'brep2sdf_{current_time}.log')
+        file_handler = logging.FileHandler(log_file, encoding='utf-8')
+        file_handler.setLevel(logging.DEBUG)
+        file_handler.setFormatter(formatter)
         
-    # 创建格式化器
+        # 创建控制台处理器
-    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        console_handler = logging.StreamHandler(sys.stdout)
+        console_handler.setLevel(logging.DEBUG)
+        console_handler.setFormatter(formatter)
+        
+        # 添加处理器
+        self.logger.addHandler(file_handler)
+        self.logger.addHandler(console_handler)
+        
+        # 记录初始信息
+        self.logger.info("="*50)
+        self.logger.info("BRep Logger initialized")
+        self.logger.info(f"Log file: {log_file}")
+        self.logger.info("="*50)
     
-    # 创建文件处理器
+    def debug(self, msg):
-    current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
+        """带调用位置的调试信息"""
-    log_file = os.path.join(log_dir, f'{name}_{current_time}.log')
+        caller = traceback.extract_stack()[-2]
-    file_handler = logging.FileHandler(log_file, encoding='utf-8')
+        filename = os.path.basename(caller.filename)
-    file_handler.setLevel(logging.INFO)
+        self.logger.debug(f"{msg} (in {filename})")
-    file_handler.setFormatter(formatter)
     
-    # 添加文件处理器到日志记录器
+    def info(self, msg):
-    logger.addHandler(file_handler)
+        self.logger.info(msg)
     
-    # 记录初始信息
+    def warning(self, msg):
-    logger.info("="*50)
+        self.logger.warning(msg)
-    logger.info(f"Logger initialized: {name}")
-    logger.info(f"Log file: {log_file}")
-    logger.info("="*50)
     
-    return logger 
+    def error(self, msg, include_trace=True):
+        """错误信息，可选是否包含调用栈"""
+        if include_trace:
+            self.logger.error(msg, exc_info=True, stack_info=True)
+        else:
+            self.logger.error(msg)
+    
+    def exception(self, msg):
+        """异常信息，总是包含异常堆栈"""
+        self.logger.exception(msg)
+
+# 创建全局logger实例
+logger = BRepLogger()